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Pinar Duygulu June 2005
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Tutorial on Image Processing
Pinar DuyguluBilkent University
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Reference Material
Computer Vision – A Modern Approach, Forsyth & Poncewww.cs.berkeley.edu /~daf
Digital Image ProcessingGonzalez and Woods
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Related Links
Computer Vision Homepagewww.cs.cmu.edu/afs/cs/project/cil/www/vision.html
Compendium of visionwww.dai.ed.ac.uk/CVonline
IEEE Publications:www.ieeexplore.org
More links www.cs.bilkent.edu.tr/~duygulu/Links/CVLinks
Computer Vision course Homepagewww.cs.bilkent.edu.tr/~duygulu/Courses/CS554/
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What do you see in the picture?
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Image Representation
• Digital Images are 2D arrays (matrices) of numbers• Each pixel is a measure of the brightness (intensity of light)
– that falls on an area of an sensor (typically a CCD chip)
adapted from Octavia Camps, Penn State
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Image Representation
RGB Greyscale Binary
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Image I/O
img = imread(‘dnm.jpg')imshow(img)
size(img)90 150 3
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RGB Channels
R = img(:,:,1)G = img(:,:,2)B = img(:,:,3)imshow(R)imshow(G)imshow(B)
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Color Spaces
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Color Spaces
Luv HSV
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RGB
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HSV
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Filtering
1 1 1 1 1 1 11 1 1 1 1 1 11 1 1 1 1 1 11 1 1 1 1 1 1 X 1/ 491 1 1 1 1 1 11 1 1 1 1 1 11 1 1 1 1 1 1
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Filtering - Smoothing
1 1 1 1 1 1 11 1 1 1 1 1 11 1 1 1 1 1 11 1 1 1 1 1 1 X 1/ 491 1 1 1 1 1 11 1 1 1 1 1 11 1 1 1 1 1 1
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Filtering
-1 0 1-1 0 1-1 0 1
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Filtering – Edge Detection
-1 0 1-1 0 1-1 0 1
Sobel Operator
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Filtering – Edge Detection
-1 –1 –10 0 01 1 1
Sobel Operator
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Canny Edge Detector
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Texture
VisTex Texture Database
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Filter Banks
• Represent image using the responses of a collection of filters
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Histogram
Cou
nts
Grey-value
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Histogram
h = imhist(R)bar(h)
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Segmentation using histogram
imshow(B > 140 )
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Histogram Similarity
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Histogram Similarity
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Localized Features
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Statistics 1 2 3 255 254 2534 5 6 245 253 2501 4 2 250 251 254
253 254 249 2 2 2254 254 254 3 3 3254 254 254 4 4 4
mean2(I)> 127.7778
b = I(1:3,1:3) b = I(1:3,4:6) b = I(4:6,1:3) b = I(4:6,4:6)mean2(b) mean2(b) mean2(b) mean2(b)> 3.1111 > 251.6667 > 253.3333 > 3
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Statistics
2 254 2 253 2 249245 5 250 4 253 64 254 4 254 4 254255 2 253 1 254 33 254 3 254 3 254
250 4 254 1 251 2
mean2(I)> 127.7778
b = I(1:3,1:3) b = I(1:3,4:6) b = I(4:6,1:3) b = I(4:6,4:6)mean2(b) mean2(b) mean2(b) mean2(b)> 113.3333 > 142.1111 > 142 > 113.6667
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Segmentation
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Segmentation
adapted from Martial Hebert, CMU
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Face detection
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Moving Object Detection
